The subject matter disclosed herein relates generally to data reconstruction systems and methods, and more particularly to systems and methods to identify boundaries and estimate properties of regions of interest, particularly in soft-field reconstructions.
Soft-field tomography, such as Electrical Impedance Spectroscopy (EIS) (also referred to as Electrical Impedance Tomography (EIT)), diffuse optical tomography, elastography, and related modalities may be used to measure the internal properties of an object, such as the electrical properties of materials comprising internal structures of an object (e.g., a region of a human body). For example, in EIT systems, an estimate is made of the distribution of electrical conductivities of the internal structures. Such EIT systems reconstruct the conductivity and/or permittivity of the materials within the area or volume based on an applied excitation (e.g., current) and measured response (e.g., voltage) acquired at the surface of the area or volume. Visual distributions of the estimates can then be formed.
In soft-field tomography, conventional reconstruction algorithms rely on sensitivity matrix based algorithms, which use gradient assumptions about how the properties of interest vary throughout the area or volume being reconstructed. In particular, the sensitivity matrix assumes that the properties to be reconstructed change smoothly throughout the area or volume being reconstructed. This assumption is often a poor approximation of the distribution of properties within the actual object of interest. Accordingly, such as assumption using a gradient to define the actual distribution of the properties is often invalid. As a result, the area or volume of features of the reconstructed image is often inaccurate and multiple objects in close proximity to one another may be obscured.